Linear predictive coding electroencephalography algorithms predict Parkinson's disease mortality using out-of-sample tests.

ABSTRACT:

Background

Parkinson's disease (PD) increases mortality is difficult to predict because of its heterogeneity and the availability of very few reliable which prognostic markers.

Objectives

We used electroencephalography (EEG) and the Linear Predictive Coding EEG Algorithm for PD (LEAPD) for binary classification of 3-year mortality status and correlation between LEAPD indices and time to death.

Methods

2-minutes resting-state EEG from 94 PD patients (59 channels, 22 deceased within 3 years of recording) was used for binary classification of 3-year mortality status. Single-channel classification using a balanced dataset of 44 was performed using leave-one-out cross-validation (LOOCV). Robustness was evaluated by truncating the recordings. LOOCV Spearman's correlation co

SUBMITTER: Jamshidi S 

PROVIDER: S-EPMC12265758 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature